Operationalize a Machine Learning Microservice API

This project contains the following:

1. Linting project codes.
2. Docker for containerize the applicaion.
3. Deploy containerize application using Docker and prediction.
4. Configure Kubernetes and create a Kubernetes cluster
5. Deploy container using and prediction.
6. Installation of Kubernetes and Minikube.

Requirements

• Python 3
• Docker
• Kubernetes
• Minikube

Setup the Environment

• Create a virtualenv and activate it
• Run make install to install the necessary dependencies

Running app.py

python app.py

Linting project codes

make lint

Run applicaion with docker.

./run_docker.sh

Upload docker image

./upload_docker.sh

⚠️ Please don't forget to change dockerpath and docker ID in upload_docker.sh

Configure Kubernetes to Run locally

minikube start
kubectl get pod

After pod status change to Running

./run_kubernetes.sh

To test application via Docker or Kubernetes

./make_prediction.sh

Files

• config.yml: CircleCI configuration file.
• Makefile: includes instructions for setup, install, test and lint.
• app.py: Python application file.
• Dockerfile: Dockerfile for build and expose.
• run_docker.sh: Shell file to run Docker, locally.
• upload_docker.sh: Shell file to upload Docker image.
• run_kubernetes.sh: Shell file to run the app with kubernetes.
• make_prediction.sh: Shell file to test flask app locally.